The approach to the problem made in [1]} tries to address the issue
by augmenting the motion model with additional complexity reflecting interaction between targets. Their work
is based on the well known particle filters which approximate the
ideal Bayesian filter using Monte-Carlo methods [2]}. They,
however, replace the importance sampling step, usually present in
conventional particle filters, by an MCMC sampling step [3]}.
This is done because, to model animal interaction, filters that evaluate
the state of the multiple objects jointly are
needed. Joint particle filters with importance sampling could be used
but their complexity grows exponentially with the number of tracked objects
which makes them unusable for more than three or four targets [1]}.
